AI-Powered Multi-Stage Waste Segregation System for Indian Municipalities: An Interdisciplinary Approach

Abstract

Waste management remains one of India’s most pressing environmental challenges, with municipal corporations struggling to process over 62 million tons of waste annually at less than 30% recycling efficiency. Manual sorting methods are slow, unhygienic, and hazardous, while imported automated systems are prohibitively expensive for Indian municipalities. This paper presents an AI-Powered Multi-Stage Waste Segregation System designed specifically for Indian waste conditions through an interdisciplinary approach combining Mechanical Engineering (50%), Electronics and Telecommunication Engineering (30%), and Computer Science Engineering (20%). The proposed sys- tem employs a three-stage progressive refinement methodology: Stage 1 utilizes mechanical separation (rotating trommel sieve, pneumatic air jets, magnetic separator) for bulk sorting of 60- 70% waste; Stage 2 integrates sensor-based detection (IR, metal, capacitance, UV sensors) for moderate-precision sorting; Stage 3 implements advanced AI (YOLOv5 object detection, multi-sensor fusion, eddy current separation) for high-precision classification of hazardous and electronic waste. The system demonstrates 92- 95% sorting accuracy at a processing speed of 10 kg/minute, with a prototype cost of approximately 1.5 lakhs — 80% cheaper than imported alternatives. Experimental results validate the effectiveness of multi-sensor fusion for medical waste detection (syringes, sanitary pads) and e-waste recovery (PCBs, batteries). This work contributes toward the vision of Swachh Bharat Mission by providing an affordable, scalable, and efficient waste management solution for Indian municipalities.

Introduction

India generates about 62 million tons of municipal solid waste annually, but only around 30% is properly processed, leaving a large portion to accumulate in landfills and cause environmental and health hazards. Current waste management practices are largely manual, exposing workers to dangerous materials and achieving only moderate sorting accuracy (60–70%) at slow speeds, while imported automated systems are too expensive and poorly suited to India’s highly mixed, contaminated waste.

To address these issues, the paper proposes a low-cost AI-powered multi-stage waste segregation system designed specifically for Indian conditions. It integrates mechanical engineering, electronics, and computer science to create a three-stage process:

1. Mechanical bulk sorting removes 60–70% of waste using sieves, air jets, and magnets.
2. Sensor-based sorting uses IR, metal, capacitance, UV, and weight sensors for intermediate classification.
3. AI precision sorting uses YOLOv5 and multi-sensor fusion with robotic handling for hazardous and complex waste.

The system improves efficiency by combining mechanical pre-sorting with smart sensing and AI, achieving 92–95% sorting accuracy at 10 kg/min speed, while costing around ?1.5 lakh, significantly cheaper than imported alternatives (?20–50 lakh).

Conclusion

This paper presented a novel AI-Powered Multi-Stage Waste Segregation System designed specifically for Indian municipalities through an interdisciplinary approach combining Mechanical, ENTC, and Computer Science engineering. The system integrates mechanical bulk sorting, sensor-based classification, and AI precision sorting within a unified three-stage architecture. A. Key Contributions – Three-stage progressive refinement achieving 92.5% accuracy at 1.5L cost – Medical waste detection using UV fluorescence (91.5% accuracy) – E-waste recovery using capacitance sensors (94.2 – Multi-sensor fusion combining IR, metal, weight, and vision data (94.8% accuracy) – Open-source implementation enabling replication and modification B. Performance Summary Metric Target Achieved Sorting Accuracy 90% 92.5% Processing Speed 10 kg/min 10 kg/min Prototype Cost 2L 1.5L Waste Recovery 95% 98.5% Medical Waste Detection 90% 91.5% E-Waste Detection 90% 94.2% • Future Work The following enhancements are planned: 1) IoT Sensor Integration: – Real-time bin fill-level monitoring – Predictive maintenance for mechanical compo- nents – Remote performance monitoring 2) Solar Power Integration: – 1kW solar panel + battery backup – Off-grid operation for rural areas – Energy-efficient component selection 3) Blockchain for Waste Tracking: – Tamper-proof records of waste processing – Transparent recycling chain verification – Carbon credit documentation 4) Logistics Optimization: – Route optimization for waste collection – Integration with recycling industry demand – Dynamic pricing for recyclables 5) Mobile Application: – Real-time sorting statistics for supervisors – Alert notifications for maintenance

References

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Copyright

Copyright © 2026 Prof. S. S. Bhosale, Shripriya Karande, Priyanka Kanaki, Sanjana Narote. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.